A geographic information encryption system based on Chaos-LSTM and chaos sequence proliferation

Jia DUAN; Luanyun HU; Qiumei XIAO; Meiting LIU; Wenxin YU

doi:10.1631/FITEE.2300755

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A geographic information encryption system based on Chaos-LSTM and chaos sequence proliferation

Regular Papers | Updated：2025-04-03

- A geographic information encryption system based on Chaos-LSTM and chaos sequence proliferation
- 基于Chaos-LSTM与混沌序列增殖的地理信息数据加密系统
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 3, Pages: 427-440(2025)
- Affiliations：
  
  1.Third Surveying and Mapping Institute of Hunan Province, Changsha 410000, China
  2.Hunan Engineering Research Center of Geographic Information Security and Application, Changsha 410000, China
  3.School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
- Author bio：
  
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2300755
  CLC： TP309
- Received：07 November 2023，
  
  Revised：08 April 2024，
  
  Published：2025-03
- Accepted：
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Jia DUAN, Luanyun HU, Qiumei XIAO, et al. A geographic information encryption system based on Chaos-LSTM and chaos sequence proliferation[J]. Frontiers of information technology & electronic engineering, 2025, 26(3): 427-440.
DOI：

Jia DUAN, Luanyun HU, Qiumei XIAO, et al. A geographic information encryption system based on Chaos-LSTM and chaos sequence proliferation[J]. Frontiers of information technology & electronic engineering, 2025, 26(3): 427-440. DOI： 10.1631/FITEE.2300755.

摘要

针对传统混沌加密算法中混沌系统状态与初始状态及参数关联性强，可能导致混沌序列存在周期性的问题，结合混沌系统和LSTM神经网络构建了Chaos-LSTM模型。针对计算机的有限计算精度效应会使长混沌序列出现周期性，使其不适宜对数据量大的对象进行加密的问题，构建了混沌序列增殖（CSP）算法。结合二者，提出了基于Chaos-LSTM与混沌序列增殖的地理信息数据加密通信系统。首先，通过Chaos-LSTM模型输出具有较高谱熵（SE）复杂度的混沌序列；然后，选取较短的混沌序列，通过CSP算法增殖出匹配加密对象的混沌加密序列，并对增殖序列进行随机性分析与测试；最后，以地理图片信息为加密对象，将混沌增殖序列与扩散算法以及置乱算法结合构成加密算法，并将加密系统在ZYNQ平台中实现。软件测试与硬件实验表明该系统具有良好的保密性能与可拓展性，能用于多种加密对象的保密通信，具备良好的应用价值。

Abstract

In response to the strong correlation between the chaotic system state and initial state and parameters in traditional chaotic encryption algorithms

which may lead to periodicity in chaotic sequences

the chaos long short-term memory (Chaos-LSTM) model is constructed by combining chaotic systems with LSTM neural networks. The chaos sequence proliferation (CSP) algorithm is constructed to address the problem that the limited computational accuracy of computers can lead to periodicity in long chaotic sequences

making them unsuitable for encrypting objects with large amounts of data. By combining the Chaos-LSTM model and CSP algorithm

a geographic information encryption system is proposed. First

the Chaos-LSTM model is used to output chaotic sequences with high spectral entropy (SE) complexity. Then

a shorter chaotic sequence is selected and proliferated using the CSP algorithm to generate chaotic proliferation sequences that match the encrypted object; a randomness analysis is conducted and testing is performed on it. Finally

using geographic images as encryption objects

the chaotic proliferation sequence

along with the scrambling and diffusion algorithms

are combined to form the encryption system

which is implemented on the ZYNQ platform. The system’s excellent confidentiality performance and scalability are proved by software testing and hardware experiments

making it suitable for the confidentiality peers of various encryption objects with outstanding application value.

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references

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